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C. Albergel, W. Dorigo, R. H. Reichle, G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, L. Isaksen, R. de Jeu, and W. Wagner

floods. For many applications, global- or continental-scale soil moisture maps are needed. Among the first soil moisture analysis systems used for operational NWP was the system implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) in 1994 to prevent the land surface model (LSM) drifting to dry conditions in summer. Since then, major upgrades have been implemented in the land surface modeling and analysis systems of the high-resolution component of the Integrated Forecasting

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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

Jeu et al. 2008 ; Draper et al. 2009 , 2011 , 2012 ). At several meteorological centers, including the Canadian Meteorological Centre of Environment Canada (EC), soil moisture is inferred from short-range NWP forecast errors in screen-level temperature and humidity ( Bélair et al. 2003a ; Drusch and Viterbo 2007 ; Mahfouf et al. 2009 ). Soil moisture is used as a sink variable where errors in atmospheric forcing and the land surface model can accumulate over time ( Seuffert et al. 2004

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Sujay V. Kumar, Kenneth W. Harrison, Christa D. Peters-Lidard, Joseph A. Santanello Jr., and Dalia Kirschbaum

followed by a description of the experimental setup of the OSSE in section 3 . Section 4 presents the results, and the major conclusions are presented in section 5 . 2. Background a. Land Information System The OSSE is conducted using the capabilities of the NASA Land Information System (LIS), which is an earth science observation-driven hydrological modeling and data assimilation framework. LIS provides the modeling and computational capabilities to merge observations and model forecasts to

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Susan Frankenstein, Maria Stevens, and Constance Scott

” (wettest 30-day period in an average rainfall year), and “wet-wet” (wettest 10 days for a year having 150% or greater of average rainfall) periods ( Bullock 1994 ; Baylot et al. 2013 ). While this method can potentially provide finer-resolution information than the SMAP data, this method still does not account for specific weather events. Another approach is to use land surface models such as Noah ( Chen et al. 1996 ) and Fast All-Season Soil Strength (FASST; Frankenstein and Koenig 2004a , b , 2008

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Amy McNally, Gregory J. Husak, Molly Brown, Mark Carroll, Chris Funk, Soni Yatheendradas, Kristi Arsenault, Christa Peters-Lidard, and James P. Verdin

Noah land surface model (LSM), and 3) microwave-derived soil moisture estimates from the European Space Agency (ESA) Essential Climate Variable (ECV) project. We standardized the Noah and ECV soil moisture estimates and then used them to replace the soil moisture values in the original WRSI calculation. This section describes in detail the meteorological inputs for the Noah LSM and the soil water accounting scheme; the different soil moisture products; the calculation of the WRSI values; and

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Randal D. Koster, Gregory K. Walker, Sarith P. P. Mahanama, and Rolf H. Reichle

of improved observational networks in recent decades, however, has supported the growth of the physical-modeling approach. A now common forecast strategy involves the use of spatially distributed land surface modeling: realistic snow and soil moisture fields are used to initialize the models, which are then integrated into the forecast period with atmospheric forcing, producing streamflow forecasts along the way ( Day 1985 ). The atmospheric forcing can take the form of historical time series at

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Catherine Champagne, Andrew Davidson, Patrick Cherneski, Jessika L’Heureux, and Trevor Hadwen

-real-time measurements of soil moisture conditions at the surface. The Soil Moisture Ocean Salinity (SMOS) mission was launched in November 2009 with goals that included the direct measurement of soil moisture at the earth’s surface and the integration of these measurements into land surface models to estimate root zone soil moisture conditions ( Kerr et al. 2001 ). Beginning with the 2010 growing season and continuing to the end of the 2013 growing season, Agriculture and Agri-Food Canada (AAFC) piloted the use of

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Fan Chen, Wade T. Crow, and Dongryeol Ryu

.01 (SMUDP2; http://earth.esa.int/smos ), retrieved using the Dobson dielectric model and processed at the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) into a daily 0.25° gridded product as part of the Soil Moisture Operational Product System (SMOPS; a full description of SMOPS is available at www.ospo.noaa.gov/Products/land/smops/ ). As with ASCAT, soil moisture retrievals obtained from ascending and descending SMOS

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Husayn El Sharif, Jingfeng Wang, and Aris P. Georgakakos

resources for agricultural use is critical as agriculture demands a large fraction of total water use in the United States and the world. In 2005, irrigation in the United States consumed 128 billion gallons per day, accounting for 37% of all freshwater withdrawals and 62% of all freshwater withdrawals excluding thermoelectric withdrawals ( Kenny et al. 2009 ). The 2013 National Climate Assessment (NCA) indicates that under the A2 emissions scenario, U.S. freshwater withdrawals will increase by 25

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M. Susan Moran, Bradley Doorn, Vanessa Escobar, and Molly E. Brown

. Hydrometeor. ). The European Centre for Medium-Range Weather Forecasts (ECMWF) is involved, among other research, in an analysis of a time series of soil moisture products for global trend analysis ( Albergel et al. 2013 ). Using data from the U.S. Drought Monitor provided by the National Drought Mitigation Center, a study reported that assimilation of soil moisture information in a land surface model provided improvement in spatial patterns of drought estimates ( Kumar et al. 2014b ). New hydrologic data

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